Techniques for Better Decision Making

In high-stakes decisions, “right” and “wrong” aren’t the point. Your method for making decisions matters more than any single result. Every major choice is a bet on a particular future. Decision quality and outcome quality are two entirely different things. Our brains want tidy stories, so we judge a decision’s quality by its outcome — a bias known as resulting. A brilliant process can still produce a bad outcome because of one unlucky break. Pete Carroll’s infamous Super Bowl call to pass from the 1-yard line was statistically sound, yet it’s reviled because it ended in a game-losing interception. To escape the trap of resulting, you need a better process. The world’s best venture capitalists use repeatable frameworks that protect them from bias and focus their attention where it matters most. Their playbook starts with two disciplines: 𝟭. 𝗦𝗼𝗿𝘁 𝘆𝗼𝘂𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: 𝗢𝗻𝗲-𝗪𝗮𝘆 𝘃𝘀. 𝗧𝘄𝗼-𝗪𝗮𝘆 𝗗𝗼𝗼𝗿𝘀 Jeff Bezos uses this mental model to allocate energy: “Two-way doors” are reversible — make those decisions quickly quickly. “One-way doors” are consequential and nearly irreversible, so you should take them slow and deliberate. The first step to better decisions is knowing which door you’re facing. 𝟮. 𝗛𝘂𝗻𝘁 𝗳𝗼𝗿 𝗮𝘀𝘆𝗺𝗺𝗲𝘁𝗿𝗶𝗰 𝗯𝗲𝘁𝘀 Stop worrying about avoiding failure and start making sure your wins are big enough to make failures irrelevant. Don’t just assess the most likely outcome. Map the full range of possibilities. A bet with a 70% chance of a small loss but a 10% chance of a 100x return can be a career-defining win. Top VCs know they’ll be wrong most of the time. In fact, they’re not aiming to be right every time. They’re looking for situations where the upside of a win is exponentially larger than the downside of a loss. I’ll be diving deeper into the methodology behind high-quality decisions in my fall Maven cohort. It’s designed for entrepreneurs, investors, and exec decision makers who have to make dozens of decisions each day. Every decision is a bet on a forecast of the future. You have limited resources to figure out which prediction will have the best return in the long run. In my course, I’ll cover my 6-step process for better, faster decision making: https://bit.ly/4ljImns

𝐓𝐡𝐨𝐬𝐞 𝐖𝐡𝐨 𝐓𝐫𝐲 𝐉𝐮𝐠𝐠𝐥𝐢𝐧𝐠 𝐓𝐨𝐨 𝐌𝐚𝐧𝐲 𝐁𝐚𝐥𝐥𝐬 𝐅𝐚𝐢𝐥 𝐌𝐢𝐬𝐞𝐫𝐚𝐛𝐥𝐲. You’re juggling three balls, it feels you’ve got this. Now you’re juggling four, it’s tough but you manage. Now you’re juggling five, chaos builds. Now you’re juggling six, you drop all of them! That’s exactly how cognitive load feels. When your brain is juggling too much information and too many decisions at the same time. As a psychologist, I see this all the time. People think they’re indecisive or unproductive, but the truth is, their mental bandwidth is maxed out. 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐥𝐨𝐚𝐝 - 𝐭𝐡𝐞 𝐦𝐞𝐧𝐭𝐚𝐥 𝐰𝐞𝐢𝐠𝐡𝐭 𝐨𝐟 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐭𝐨𝐨 𝐦𝐮𝐜𝐡 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐛𝐚𝐫𝐫𝐢𝐞𝐫𝐬 𝐭𝐨 𝐜𝐥𝐞𝐚𝐫, 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐭 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠. When your brain is overwhelmed, even small decisions feel monumental. That’s why you might spend ages picking a restaurant after a day of big meetings. Your brain isn’t lazy—it’s overworked. But it’s not just about feeling tired. Cognitive load impacts the quality of your decisions. The more overwhelmed you are, the more likely you are to choose what’s easy, familiar, or convenient, not necessarily what’s best. Sounds scary. Right? I’ve worked with clients who felt stuck, unable to decide between career moves, new opportunities, or even personal goals. Most of the time, the problem wasn’t indecision. It was the sheer amount of information and options clouding their minds. 𝐒𝐨, 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐥𝐢𝐠𝐡𝐭𝐞𝐧 𝐭𝐡𝐞 𝐦𝐞𝐧𝐭𝐚𝐥 𝐥𝐨𝐚𝐝 𝐚𝐧𝐝 𝐦𝐚𝐤𝐞 𝐛𝐞𝐭𝐭𝐞𝐫 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬? → 𝐋𝐢𝐦𝐢𝐭 𝐘𝐨𝐮𝐫 𝐈𝐧𝐩𝐮𝐭𝐬: Be selective about what you consume. Your brain wasn’t designed to process infinite notifications or social feeds. Filter and focus. → 𝐁𝐚𝐭𝐜𝐡 𝐒𝐢𝐦𝐢𝐥𝐚𝐫 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬: Make decisions in clusters. Planning your week’s meals in one go is far less taxing than deciding every day. → 𝐒𝐞𝐭 𝐁𝐨𝐮𝐧𝐝𝐚𝐫𝐢𝐞𝐬: Not every choice deserves endless time. Give yourself limits. Trust your instincts and move forward. One client came to me overwhelmed by decisions, from strategic career moves to daily operations. We simplified her processes, grouped her tasks, and gave her decision-making space. Within weeks, she felt clearer, more confident, and far more in control. Cognitive load isn’t something you can escape entirely, but you can manage it. By reducing the mental clutter, you create space for clarity, confidence, and focus. If this clicks with you, I’d be delighted to share more insights into the psychology of decision-making with your team! Let’s get talking! #decisionmaking #team #mentalhealth #career #psychology #personaldevelopment

Researchers at UC San Diego and Tsinghua just solved a major challenge in making LLMs reliable for scientific tasks: knowing when to use tools versus solving problems directly. Their method, called Adapting While Learning (AWL), achieves this through a novel two-component training approach: (1) World knowledge distillation - the model learns to solve problems directly by studying tool-generated solutions (2) Tool usage adaptation - the model learns to intelligently switch to tools only for complex problems it can't solve reliably The results are impressive: * 28% improvement in answer accuracy across scientific domains * 14% increase in tool usage precision * Strong performance even with 80% noisy training data * Outperforms GPT-4 and Claude on custom scientific datasets Current approaches either make LLMs over-reliant on tools or prone to hallucinations when solving complex problems. This method mimics how human experts work - first assessing if they can solve a problem directly before deciding to use specialized tools. Paper https://lnkd.in/g37EK3-m — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai

LLMs struggle with rationality in complex game theory situations, which are very common in the real world. However integrating structured game theory workflows into LLMs enables them to compute and execute optimal strategies such as Nash Equilibria. This will be vital for bringing AI into real-world situations, especially with the rise of agentic AI. The paper "Game-theoretic LLM: Agent Workflow for Negotiation Games" (link in comments) examines the performance of LLMs in strategic games and how to improve them. Highlights from the paper: 💡 Strategic Limitations of LLMs in Game Theory: LLMs struggle with rationality in complex game scenarios, particularly as game complexity increases. Despite their ability to process large amounts of data, LLMs often deviate from Nash Equilibria in games with larger payoff matrices or sequential decision trees. This limitation suggests a need for structured guidance to improve their strategic reasoning capabilities. 🔄 Workflow-Driven Rationality Improvements: Integrating game-theoretic workflows significantly enhances the performance of LLMs in strategic games. By guiding decision-making with principles like Nash Equilibria, Pareto optimality, and backward induction, LLMs showed improved ability to identify optimal strategies and robust rationality even in negotiation scenarios. 🤝 Negotiation as a Double-Edged Sword: Negotiations improved outcomes in coordination games but sometimes led LLMs away from Nash Equilibria in scenarios where these equilibria were not Pareto optimal. This reflects a tendency for LLMs to prioritize fairness or trust over strict game-theoretic rationality when engaging in dialogue with other agents. 🌐 Challenges with Incomplete Information: In incomplete-information games, LLMs demonstrated difficulty handling private valuations and uncertainty. Novel workflows incorporating Bayesian belief updating allowed agents to reason under uncertainty and propose envy-free, Pareto-optimal allocations. However, these scenarios highlighted the need for more nuanced algorithms to account for real-world negotiation dynamics. 📊 Model Variance in Performance: Different LLM models displayed varying levels of rationality and susceptibility to negotiation-induced deviations. For instance, model o1 consistently adhered more closely to Nash Equilibria compared to others, underscoring the importance of model-specific optimization for strategic tasks. 🚀 Practical Implications: The findings suggest LLMs can be optimized for strategic applications like automated negotiation, economic modeling, and collaborative problem-solving. However, careful design of workflows and prompts is essential to mitigate their inherent biases and enhance their utility in high-stakes, interactive environments.

😅 The biggest AI coding question today: how do you get to production without shipping vibe coded slop? A lot of developers now understand that AI can help them move fast. You can vibe code a prototype, generate a feature quickly, and get a working draft in minutes. But the harder question is, how do you make sure the code actually stands the test of time? This is exactly what Mihail Eric covered in our internal Chai & AI community session last week. Mihail teaches the "The Modern Software Developer" course at Stanford, is Head of AI at Monaco, and was also a colleague of mine back at Alexa. In the session, he introduced his RePPIT framework for using AI to ship production quality code. RePPIT breaks down the coding process into five deliberate steps: (Re)search, (P)ropose, (P)lan, (I)mplement, and (T)est. A few ideas from the RePPIT framework, although I’d recommend reading the whole the whole article we wrote around the session (linked below): ⛳ Research the codebase: Before asking AI to implement anything, first ask it to understand the architecture, file layout, dependencies, design decisions, and existing patterns. This keeps the model grounded in the actual codebase. ⛳ Propose solutions: Instead of jumping straight into code, ask the model to generate a couple of different implementation paths with tradeoffs, validation plans, and open questions. This is where you, as the developer, make the judgment call. ⛳ Plan the chosen solution: Once you pick a direction, turn it into a proper design doc. This helps define what’s in scope, what’s out of scope, what files need to change, and how the feature should be tested. ⛳ Implement the plan: Only after the research, proposal, and planning steps should the model start writing code. At this point, it has enough context to avoid guessing its way through the implementation. ⛳ Test what got built: Don’t let the same model instance blindly approve its own work. Use a fresh context or a different model to review, test, and critique the implementation before you trust it. I think a lot of this boils down to strong context engineering. It was also super useful to walk through a concrete example, which you can see in the article. Article: https://lnkd.in/gG9uF6kC Mihail also runs an incredibly cool course at Stanford as well as on Maven. You can find links to both below!

Your research findings are useless if they don't drive decisions. After watching countless brilliant insights disappear into the void, I developed 5 practical templates I use to transform research into action: 1. Decision-Driven Journey Map Standard journey maps look nice but often collect dust. My Decision-Driven Journey Map directly connects user pain points to specific product decisions with clear ownership. Key components: - User journey stages with actions - Pain points with severity ratings (1-5) - Required product decisions for each pain - Decision owner assignment - Implementation timeline This structure creates immediate accountability and turns abstract user problems into concrete action items. 2. Stakeholder Belief Audit Workshop Many product decisions happen based on untested assumptions. This workshop template helps you document and systematically test stakeholder beliefs about users. The four-step process: - Document stakeholder beliefs + confidence level - Prioritize which beliefs to test (impact vs. confidence) - Select appropriate testing methods - Create an action plan with owners and timelines When stakeholders participate in this process, they're far more likely to act on the results. 3. Insight-Action Workshop Guide Research without decisions is just expensive trivia. This workshop template provides a structured 90-minute framework to turn insights into product decisions. Workshop flow: - Research recap (15min) - Insight mapping (15min) - Decision matrix (15min) - Action planning (30min) - Wrap-up and commitments (15min) The decision matrix helps prioritize actions based on user value and implementation effort, ensuring resources are allocated effectively. 4. Five-Minute Video Insights Stakeholders rarely read full research reports. These bite-sized video templates drive decisions better than documents by making insights impossible to ignore. Video structure: - 30 sec: Key finding - 3 min: Supporting user clips - 1 min: Implications - 30 sec: Recommended next steps Pro tip: Create a library of these videos organized by product area for easy reference during planning sessions. 5. Progressive Disclosure Testing Protocol Standard usability testing tries to cover too much. This protocol focuses on how users process information over time to reveal deeper UX issues. Testing phases: - First 5-second impression - Initial scanning behavior - First meaningful action - Information discovery pattern - Task completion approach This approach reveals how users actually build mental models of your product, leading to more impactful interface decisions. Stop letting your hard-earned research insights collect dust. I’m dropping the first 3 templates below, & I’d love to hear which decision-making hurdle is currently blocking your research from making an impact! (The data in the templates is just an example, let me know in the comments or message me if you’d like the blank versions).

If there's one meeting ritual we use more than any other at Superhuman, it's what we call the "Dory." It's a simple ranking tool where everyone can add questions and upvote/downvote them, and it’s incredibly impactful for getting to better decisions by equalizing voices. Here’s how it works: everyone adds their questions to a shared table, votes on the ones they care about, and then you work through the list in order of votes, regardless of who asked or how senior they are. My favorite tip is to ask the person who wrote each question to read it out loud. It keeps the meeting human, and it gives them a chance to add context that the written question didn't capture. As I've worked with teams that adopted the ritual, I've seen dozens of variations because it’s great not only for Q&A, but also for selecting agenda topics or even brainstorming. A few other common ways to use Dory: 1️⃣ Cap the votes. Give everyone a maximum number of votes they can use. When people can vote for everything, you lose the signal on priority entirely, so capping it at two or three makes each vote feel like it matters. One tip I have is to match the number of votes to the number of things the team actually has capacity for, which feels more natural than an arbitrary limit. 2️⃣ Use sentence starters. Give people a prompt to fill in, like "I like..." or "I wish..." When you just ask a group what they think, they tend to either stay polite and unhelpful or go straight for the jugular. Sentence starters give people the structure to say the honest thing in a way the room can actually hear. 3️⃣ Categorize as you go. Have the meeting driver sort the incoming questions into buckets while people add and vote. When it's time to discuss, you can group by category and start with the area that drew the most interest, which gives the driver a clean way to steer the conversation toward what the room cares about. I use Dory probably over a thousand times a year, and one thing to keep in mind is that the purpose isn’t just to be a “count the votes” decision-making style. The ranking organizes the conversation and makes sure every voice is heard, and how you then make a decision is up to you. And yes, Dory was named after the Finding Nemo fish who asks all the questions!

"Do you have budget? Who makes decisions? What's your timeline?" Your prospect just mentally checked out. They've heard these same three questions from every vendor who's called them this week. You're losing deals because you're QUALIFYING instead of DISCOVERING. After coaching hundreds of reps who crush small deals but lose the big ones, I've identified the #1 mistake in enterprise sales: → Treating discovery like a vendor interrogation instead of a trusted advisor conversation. Here's the reality: 10% of prospects will never buy, 10% will always buy, and 80% can be swayed either way. That middle 80%? They're won or lost in discovery. Most reps ask surface level questions and move on: "We're losing customers." "Got it. Next question." But top performers go DEEP: "How many customers exactly? What's the revenue per customer? What's your current churn rate? How does losing customers impact your ability to hit growth targets?" Suddenly you're not solving a "customer retention issue." You're solving a $300K annual revenue leak that's preventing them from hitting their board commitments. This is why I developed the POWERFUL framework: P - Pain  O - Opportunity cost  W - Wants and desires  E - Executive influence  R - Resources  F - Fear of failure  U - Unequivocal trust L- Little stuff" When prospects believe at a level 10 in all eight areas, deals roll fast. The hardest territory to manage is the one between your ears. When you change your mindset from "Do they qualify?" to "How can I understand their world?", you'll start winning those 6 and 7-figure deals you've been losing. One of my clients, Samantha, went from struggling with mid-market to closing 10 Fortune 500 logos in 5 months using this framework. Cold to close. Remember: Prospects don't buy from vendors who qualify them. They buy from advisors who understand them. Sales leaders: Stop training your reps to run through checklists. Train them to pull threads and go deep. Discovery isn't a step in your process - it's embedded in every conversation until close. — Reps: Book your call now to get the EXACT blueprint elite reps use to crush their quotas. https://lnkd.in/gr9u5Vgd Sales leaders: If you're serious about building a sales machine that consistently doubles results in 90 days, visit https://lnkd.in/ghh8VCaf

Too many reps are still winging their meetings. They show up. They talk too much. They forget to follow up. And then they wonder why deals stall. The top reps I’ve trained over the years all have one thing in common: They don’t just HAVE better meetings, they RUN better meetings. Here’s what they do differently: • They send a shared agenda before every call • They follow the 40/40/20 structure to guide the conversation - 40% Discovery (focused on impact) - 40% Value Alignment (From the executives priorities down) - 20% Next Steps (dates, owners, action items) • They send summary emails with key take-aways and get the client to confirm it • They use tools like Otter.ai to automate the admin and focus on selling The best part? You can steal these frameworks for free. This is the Sales Conversation Playbook I built with Otter.ai: https://lnkd.in/eKxVwep4 It’s short, tactical, and loaded with tools you can use right now to increase conversion. Meetings don’t move deals. Clear next steps do. Use some of the tips and tools in this workbook to run your next call and let me know if it makes a difference. #MakeItHappen #MeetingExecution #sponsor #Discovery

"What if I make the wrong decision?" "What if users hate my product?" "What do I tell my manager?" Every product manager sometimes fears making decisions because our decisions have long-lasting and drastic impact on our users and the business. If you fear making a decision, the solution is 𝗡𝗢𝗧 to avoid it. Instead, it is to make the "𝗯𝗲𝘀𝘁 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲" given the knowledge, information, and experience you have. When I am in situations where I need to make a critical decision with limited information, this is what I do: 𝗚𝗮𝘁𝗵𝗲𝗿 𝗮𝘀 𝗺𝘂𝗰𝗵 𝗱𝗮𝘁𝗮 𝗮𝘀 𝗜 𝗰𝗮𝗻 I gather more information via user research, market analysis, stakeholder input, and competitive analysis. The more information I have, the better the decision. 𝗖𝗹𝗲𝗮𝗿𝗹𝘆 𝗱𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗜 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 This helps me focus on the most critical decisions. It helps me not get distracted by irrelevant/less important aspects. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗹𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝘃𝗲𝘀. I like to, first, think of multiple options. Then I weigh the pros and cons of all options using as much data and information as possible. This approach forces me to objectively think of the positive impact and compare it to the potential risks. This improves my decision quality. 𝗖𝗼𝗻𝘀𝘂𝗹𝘁 𝗼𝘁𝗵𝗲𝗿𝘀 𝘁𝗼 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗺𝘆 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. Different perspectives expose me to ideas I wouldn't have thought of alone. These new ideas make my decision more thorough. 𝗠𝗮𝗸𝗲 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗮𝗻𝗱 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗶𝘁 There is never a perfect time to make a decision. When I have the information I can get quickly, I go ahead and make the decision. I then document my approach, reasoning, and rationale for making the decision. This document acts as a quick reference for later and keeps improving my decision-making process. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲, 𝗰𝗼𝘂𝗿𝘀𝗲 𝗰𝗼𝗿𝗿𝗲𝗰𝘁, 𝗶𝗺𝗽𝗿𝗼𝘃𝗲. Even if I make one wrong decision, that does not always mean that all future decisions will be wrong, so I stop, evaluate, measure, and improve after every decision. -- In most situations, PMs will never have the perfect information required to make the perfect decision. So, always aim to make the "best decision" based on the information you have. Data, logic, open-mindedness, and critical thinking help make the "best decision possible" in most situations. Remember: Perfection is not the goal. Progress is.